This will instead include a document summary section, a project summary, and links to other important documentation.
Model code example
Show the code
## RTMB dat and par setup ##### Datdat <-list(srdat = srdat,WAbase = WAbase,WAin = WAin,lineWA =seq(min(WAbase$logWAshifted), max(WAbase$logWAshifted), 0.1), # Not added to NLLmean_logWA = mean_logWA,logRS =log(srdat$Rec) -log(srdat$Sp),prioronly =0) # 0-run with data, 1-prior prediction mode# External vectorsN_Stk <-max(srdat$Stocknumber +1)N_Obs <-nrow(srdat)stk = srdat$Stocknumber +1lhdiston <- T bias.cor <- F # Parameters/Initial valuespar <-list(b0 =c(10, 0), # Initial values for WA regression interceptsbWA =c(0, 0), # Inital values for WA regression slopeslogSREP_re =numeric(N_Stk), # ZeroeslogAlpha0 =0.6,logAlpha_re =numeric(nrow(dat$WAbase)), # Zeroestauobs =0.01+numeric(N_Stk), # Constrained positivelogSREP_sd =1, logAlpha_sd =1)if (lhdiston) { par$logAlpha02 <-0}f_srep <-function(par){getAll(dat, par) N_Stk =max(srdat$Stocknumber +1) # number of stocks stk = srdat$Stocknumber +1# vector of stocknumbers N_Obs =nrow(srdat) # number of observations N_Pred =nrow(WAin) # number of predicted watershed areas S = srdat$Sp type = lifehist$lh type_tar =as.numeric(WAin$lh) SREP <-numeric(N_Stk)# logE_pred <- numeric(N_Stk) logSREP <-numeric(N_Stk) logAlpha <-numeric(N_Stk) logRS_pred <-numeric(N_Obs) SREP_tar <-numeric(N_Pred) logSREP_tar <-numeric(N_Pred) logAlpha_tar <-numeric(N_Pred)# Simulated line vectors line <-length(lineWA) logSREP_line_stream <-numeric(line) SREP_line_stream <-numeric(line) logSREP_line_ocean <-numeric(line) SREP_line_ocean <-numeric(line)if (bias.cor) { biaslogSREP <--0.5*logSREP_sd^2 biaslogAlpha <--0.5*logAlpha_sd^2 biaslogRS <--0.5*(sqrt(1/tauobs))^2 } else { biaslogSREP <-0 biaslogAlpha <-0 biaslogRS <-numeric(N_Stk) } nll <-0# Begin negative log-likelihood nll <- nll -sum(dnorm(b0[1], 10, sd =31.6, log =TRUE)) # Prior nll <- nll -sum(dnorm(b0[2], 0, sd =31.6, log =TRUE)) # Prior nll <- nll -sum(dnorm(bWA[1], 0, sd =31.6, log =TRUE)) # Prior nll <- nll -sum(dnorm(bWA[2], 0, sd =31.6, log =TRUE)) # Prior nll <- nll -sum(dnorm(logAlpha0, 0.6, sd =0.45, log =TRUE)) # Prior (rM)if(lhdiston) nll <- nll -sum(dnorm(logAlpha02, 0, sd =31.6, log =TRUE)) # Prior (rD)## Second level of hierarchy - Ricker parameters:for (i in1:N_Stk){ nll <- nll -dnorm(logSREP_re[i], 0, sd =1, log =TRUE) logSREP[i] <- b0[1] + b0[2]*type[i] + (bWA[1] + bWA[2]*type[i]) * WAbase$logWAshifted[i] + logSREP_re[i]*logSREP_sd + biaslogSREP SREP[i] <-exp(logSREP[i]) nll <- nll -dnorm(logAlpha_re[i], 0, sd =1, log =TRUE) if(lhdiston) logAlpha[i] <- logAlpha0 + logAlpha02*type[i] + logAlpha_re[i]*logAlpha_sd + biaslogAlphaelse logAlpha[i] <- logAlpha0 + logAlpha_re[i]*logAlpha_sd + biaslogAlpha nll <- nll -dgamma(tauobs[i], shape =0.0001, scale =1/0.0001, log =TRUE) }## First level of hierarchy: Ricker model:for (i in1:N_Obs){ logRS_pred[i] <- logAlpha[stk[i]]*(1- S[i]/SREP[stk[i]]) + biaslogRS[stk[i]]if(!prioronly){ nll <- nll -dnorm(logRS[i], logRS_pred[i], sd =sqrt(1/tauobs[stk[i]]), log =TRUE) } }## Calculate SMSY for Synoptic set - for plotting SMSY_r =numeric(nrow(WAbase)) BETA_r =numeric(nrow(WAbase))for (i in1:N_Stk){ BETA_r[i] <- logAlpha[i] / SREP[i] SMSY_r[i] <- (1-LambertW0(exp(1- logAlpha[i]))) / BETA_r[i] }## PREDICTIONS BETA =numeric(nrow(WAin)) SMSY =numeric(nrow(WAin)) SGEN =numeric(nrow(WAin))for (i in1:N_Pred){if(lhdiston) logAlpha_tar[i] <- logAlpha0 + logAlpha02*type_tar[i] + biaslogAlpha else logAlpha_tar[i] <- logAlpha0 + biaslogAlpha logSREP_tar[i] <- b0[1] + b0[2]*type_tar[i] + (bWA[1] + bWA[2]*type_tar[i])*WAin$logWAshifted_t[i] + biaslogSREP SREP_tar[i] <-exp(logSREP_tar[i])# Predict BETA BETA[i] <- logAlpha_tar[i]/SREP_tar[i]# Predict SMSY SMSY[i] <- (1-LambertW0(exp(1-logAlpha_tar[i])))/BETA[i]# Predict SGEN SGEN[i] <--1/BETA[i]*LambertW0(-BETA[i]*SMSY[i]/(exp(logAlpha_tar[i]))) }# Create predictions on an simulated linefor (i in1:line){ logSREP_line_ocean[i] <- b0[1] + b0[2] + (bWA[1] + bWA[2])*lineWA[i] + biaslogSREP # DATA - not in likelihood SREP_line_ocean[i] <-exp(logSREP_line_ocean[i]) logSREP_line_stream[i] <- b0[1] + (bWA[1])*lineWA[i] + biaslogSREP # DATA - not in likelihood SREP_line_stream[i] <-exp(logSREP_line_stream[i]) }## ADREPORT - internal values (synoptic specific/Ricker) REPORT(b0) # Testing simulate()REPORT(bWA) # Testing simulate()REPORT(logRS_pred) alpha <-exp(logAlpha)# REPORT(logRS) # logRS for all 501 data pointsREPORT(logSREP_re)REPORT(logSREP_sd)REPORT(SREP) # E (Srep) for all synoptic data set rivers (25)REPORT(logSREP)REPORT(logAlpha) # model logAlpha (25)REPORT(logAlpha0)REPORT(logAlpha02)REPORT(logAlpha_re) # random effect parameter for resamplingREPORT(logAlpha_sd)REPORT(alpha)REPORT(SMSY_r)REPORT(BETA_r)REPORT(tauobs) # Necessary to add back in observation error? alpha_tar <-exp(logAlpha_tar)REPORT(SREP_tar)REPORT(logSREP_tar)REPORT(logAlpha_tar)REPORT(alpha_tar)REPORT(BETA)REPORT(SMSY)REPORT(SGEN)# Simulated line values for plottingREPORT(SREP_line_stream) REPORT(logSREP_line_stream) REPORT(SREP_line_ocean) REPORT(logSREP_line_ocean) nll # output of negative log-likelihood}
Extended Results Section: Figures and Tables
1. Model Parameter Trace Plots
b0
b0[1]
b0[2]
bWA
bWA[1]
bWA[2]
logAlpha_re
logAlpha_re[1]
logAlpha_re[2]
logAlpha_re[3]
logAlpha_re[4]
logAlpha_re[5]
logAlpha_re[6]
logAlpha_re[7]
logAlpha_re[8]
logAlpha_re[9]
logAlpha_re[10]
logAlpha_re[11]
logAlpha_re[12]
logAlpha_re[13]
logAlpha_re[14]
logAlpha_re[15]
logAlpha_re[16]
logAlpha_re[17]
logAlpha_re[18]
logAlpha_re[19]
logAlpha_re[20]
logAlpha_re[21]
logAlpha_re[22]
logAlpha_re[23]
logAlpha_re[24]
logAlpha_re[25]
logAlpha_sd
logAlpha_sd
logAlpha0
logAlpha0
logAlpha02
logAlpha02
logSREP_re
logSREP_re[1]
logSREP_re[2]
logSREP_re[3]
logSREP_re[4]
logSREP_re[5]
logSREP_re[6]
logSREP_re[7]
logSREP_re[8]
logSREP_re[9]
logSREP_re[10]
logSREP_re[11]
logSREP_re[12]
logSREP_re[13]
logSREP_re[14]
logSREP_re[15]
logSREP_re[16]
logSREP_re[17]
logSREP_re[18]
logSREP_re[19]
logSREP_re[20]
logSREP_re[21]
logSREP_re[22]
logSREP_re[23]
logSREP_re[24]
logSREP_re[25]
logSREP_sd
logSREP_sd
lp__
lp__
tauobs
tauobs[1]
tauobs[2]
tauobs[3]
tauobs[4]
tauobs[5]
tauobs[6]
tauobs[7]
tauobs[8]
tauobs[9]
tauobs[10]
tauobs[11]
tauobs[12]
tauobs[13]
tauobs[14]
tauobs[15]
tauobs[16]
tauobs[17]
tauobs[18]
tauobs[19]
tauobs[20]
tauobs[21]
tauobs[22]
tauobs[23]
tauobs[24]
tauobs[25]
2. Model Parameter Autocorrelation Plots
b0
b0[1]
b0[2]
bWA
bWA[1]
bWA[2]
logAlpha_re
logAlpha_re[1]
logAlpha_re[2]
logAlpha_re[3]
logAlpha_re[4]
logAlpha_re[5]
logAlpha_re[6]
logAlpha_re[7]
logAlpha_re[8]
logAlpha_re[9]
logAlpha_re[10]
logAlpha_re[11]
logAlpha_re[12]
logAlpha_re[13]
logAlpha_re[14]
logAlpha_re[15]
logAlpha_re[16]
logAlpha_re[17]
logAlpha_re[18]
logAlpha_re[19]
logAlpha_re[20]
logAlpha_re[21]
logAlpha_re[22]
logAlpha_re[23]
logAlpha_re[24]
logAlpha_re[25]
logAlpha_sd
logAlpha_sd
logAlpha0
logAlpha0
logAlpha02
logAlpha02
logSREP_re
logSREP_re[1]
logSREP_re[2]
logSREP_re[3]
logSREP_re[4]
logSREP_re[5]
logSREP_re[6]
logSREP_re[7]
logSREP_re[8]
logSREP_re[9]
logSREP_re[10]
logSREP_re[11]
logSREP_re[12]
logSREP_re[13]
logSREP_re[14]
logSREP_re[15]
logSREP_re[16]
logSREP_re[17]
logSREP_re[18]
logSREP_re[19]
logSREP_re[20]
logSREP_re[21]
logSREP_re[22]
logSREP_re[23]
logSREP_re[24]
logSREP_re[25]
logSREP_sd
logSREP_sd
lp__
lp__
tauobs
tauobs[1]
tauobs[2]
tauobs[3]
tauobs[4]
tauobs[5]
tauobs[6]
tauobs[7]
tauobs[8]
tauobs[9]
tauobs[10]
tauobs[11]
tauobs[12]
tauobs[13]
tauobs[14]
tauobs[15]
tauobs[16]
tauobs[17]
tauobs[18]
tauobs[19]
tauobs[20]
tauobs[21]
tauobs[22]
tauobs[23]
tauobs[24]
tauobs[25]
3. Model Parameter Pairs Plots
Show figure: Pairs Plots
4. Convergence Diagnostic Statistics Table
Geweke Statistics
Show table
Parameter
Chain1
Chain2
Chain3
Chain4
b0[1]
-1.478
0.678
0.526
-0.176
b0[2]
2.562
-0.712
-0.572
-0.429
bWA[1]
-0.801
-2.093
-0.692
0.380
bWA[2]
0.839
-0.174
0.621
-0.302
logSREP_re[1]
-0.818
1.114
0.657
0.573
logSREP_re[2]
1.066
0.488
0.653
0.050
logSREP_re[3]
1.860
-0.048
1.489
-0.338
logSREP_re[4]
0.767
-1.123
1.806
0.629
logSREP_re[5]
0.306
-0.671
-0.744
-0.018
logSREP_re[6]
0.982
-1.024
-0.916
0.026
logSREP_re[7]
0.417
-1.477
-1.103
0.610
logSREP_re[8]
-0.196
-0.085
-1.257
0.724
logSREP_re[9]
0.388
-1.350
-0.286
0.573
logSREP_re[10]
-1.321
1.816
0.654
1.200
logSREP_re[11]
-0.518
-0.574
0.020
0.816
logSREP_re[12]
-0.617
-1.143
0.052
-0.014
logSREP_re[13]
-1.825
0.620
-0.257
0.693
logSREP_re[14]
-0.787
0.104
0.793
0.414
logSREP_re[15]
0.897
-1.892
-0.564
0.376
logSREP_re[16]
2.517
-0.231
-0.177
0.510
logSREP_re[17]
1.308
0.643
0.496
-0.651
logSREP_re[18]
1.948
-0.457
0.574
0.874
logSREP_re[19]
0.772
0.291
1.259
-0.053
logSREP_re[20]
-1.245
1.202
-1.241
-0.873
logSREP_re[21]
-0.043
-0.605
-0.307
1.297
logSREP_re[22]
-1.431
1.576
1.504
1.099
logSREP_re[23]
0.455
0.880
-0.183
-0.187
logSREP_re[24]
-1.952
-0.265
-1.027
-0.456
logSREP_re[25]
-1.964
0.488
1.715
0.954
logAlpha0
-0.735
-1.040
-1.453
-0.998
logAlpha_re[1]
0.496
-1.772
0.447
-0.896
logAlpha_re[2]
-1.449
-1.607
-0.566
0.416
logAlpha_re[3]
-0.659
-0.046
2.075
0.385
logAlpha_re[4]
1.020
0.848
-1.472
-0.252
logAlpha_re[5]
2.105
1.101
3.849
1.352
logAlpha_re[6]
0.097
-0.491
1.364
0.508
logAlpha_re[7]
-0.099
-0.371
0.169
-0.169
logAlpha_re[8]
0.017
-1.036
1.216
-0.406
logAlpha_re[9]
2.250
1.114
1.575
-0.903
logAlpha_re[10]
1.263
-1.551
-1.220
0.704
logAlpha_re[11]
2.610
0.993
-0.120
-0.493
logAlpha_re[12]
0.767
0.492
-0.501
0.573
logAlpha_re[13]
0.125
0.646
0.694
0.712
logAlpha_re[14]
0.210
0.193
0.291
0.395
logAlpha_re[15]
0.577
1.760
0.516
1.587
logAlpha_re[16]
0.077
-0.221
-1.000
-1.796
logAlpha_re[17]
2.545
1.574
1.468
0.702
logAlpha_re[18]
0.738
0.426
-0.741
-0.436
logAlpha_re[19]
1.292
0.116
1.032
0.058
logAlpha_re[20]
-0.365
-1.227
0.265
0.080
logAlpha_re[21]
0.688
1.061
0.501
-0.636
logAlpha_re[22]
0.566
-0.771
-1.244
0.331
logAlpha_re[23]
-0.168
0.017
0.738
0.945
logAlpha_re[24]
0.037
-0.013
-0.829
0.204
logAlpha_re[25]
0.616
0.099
0.670
-0.337
tauobs[1]
-0.205
1.222
0.010
-0.569
tauobs[2]
1.568
-0.863
0.456
-0.876
tauobs[3]
-1.845
0.568
0.004
0.629
tauobs[4]
0.277
1.259
-1.279
0.874
tauobs[5]
1.070
0.863
-0.809
-0.430
tauobs[6]
-0.735
-0.466
0.022
0.426
tauobs[7]
-2.194
1.267
2.178
-0.725
tauobs[8]
-0.242
-1.496
-0.593
-0.645
tauobs[9]
1.533
0.561
-0.382
-0.983
tauobs[10]
0.795
-1.397
1.576
0.233
tauobs[11]
-1.035
1.485
-1.533
-1.563
tauobs[12]
0.074
0.298
-2.962
0.983
tauobs[13]
-1.569
-0.592
-0.015
0.651
tauobs[14]
-1.562
0.584
0.871
1.600
tauobs[15]
1.728
1.924
0.091
0.785
tauobs[16]
-0.599
0.741
-0.900
0.641
tauobs[17]
1.460
0.869
0.859
-0.110
tauobs[18]
-0.290
-0.411
-0.026
-1.581
tauobs[19]
1.236
-0.732
1.602
-1.161
tauobs[20]
1.167
1.049
0.405
0.951
tauobs[21]
-1.089
0.784
0.415
0.632
tauobs[22]
0.958
-1.053
-1.097
-0.504
tauobs[23]
-0.362
-0.951
-0.283
-0.944
tauobs[24]
-0.168
1.219
0.455
-1.063
tauobs[25]
0.054
1.521
-0.086
-1.065
logSREP_sd
-0.910
-0.970
0.165
-0.171
logAlpha_sd
1.016
2.006
1.245
0.624
logAlpha02
-0.493
0.573
1.011
0.186
lp__
-0.928
1.020
0.149
0.491
Show Chain 1Show Chain 2Show Chain 3Show Chain 4
Heidelberg etc. Statistics
Show Chain 1
stest
start
pvalue
htest
mean
halfwidth
b0[1]
1
1
0.256
1
8.941
0.010
b0[2]
1
251
0.458
1
1.042
0.015
bWA[1]
1
1
0.394
1
0.680
0.004
bWA[2]
1
1
0.442
1
0.281
0.010
logSREP_re[1]
1
1
0.363
1
0.758
0.043
logSREP_re[2]
1
1
0.448
0
-0.037
0.032
logSREP_re[3]
1
1
0.142
1
1.096
0.033
logSREP_re[4]
1
1
0.917
1
-0.970
0.032
logSREP_re[5]
1
1
0.448
1
-0.740
0.030
logSREP_re[6]
1
1
0.331
1
0.404
0.035
logSREP_re[7]
1
1
0.391
0
0.162
0.035
logSREP_re[8]
1
1
0.335
1
-1.076
0.044
logSREP_re[9]
1
1
0.426
1
0.724
0.037
logSREP_re[10]
1
1
0.419
1
0.433
0.028
logSREP_re[11]
1
1
0.745
0
-0.372
0.042
logSREP_re[12]
1
1
0.834
1
0.756
0.030
logSREP_re[13]
1
1
0.326
1
-0.471
0.025
logSREP_re[14]
1
1
0.769
1
0.913
0.028
logSREP_re[15]
1
1
0.306
0
0.295
0.032
logSREP_re[16]
1
1
0.346
1
1.599
0.030
logSREP_re[17]
1
1
0.375
0
0.048
0.045
logSREP_re[18]
1
251
0.287
1
-0.756
0.033
logSREP_re[19]
1
1
0.715
1
-0.536
0.032
logSREP_re[20]
1
1
0.125
1
-0.862
0.039
logSREP_re[21]
1
1
0.386
1
0.809
0.032
logSREP_re[22]
1
1
0.505
1
-1.269
0.029
logSREP_re[23]
1
1
0.992
0
0.350
0.036
logSREP_re[24]
1
1
0.402
1
-0.364
0.030
logSREP_re[25]
1
1
0.758
1
-0.536
0.028
logAlpha0
1
1
0.521
1
1.560
0.006
logAlpha_re[1]
1
1
0.824
0
-0.337
0.036
logAlpha_re[2]
1
1
0.617
1
-0.762
0.036
logAlpha_re[3]
0
NA
0.005
NA
NA
NA
logAlpha_re[4]
1
1
0.487
1
-0.359
0.035
logAlpha_re[5]
1
1
0.213
0
0.301
0.032
logAlpha_re[6]
1
1
1.000
1
-0.474
0.034
logAlpha_re[7]
1
1
0.723
0
-0.267
0.031
logAlpha_re[8]
1
1
0.851
0
-0.049
0.042
logAlpha_re[9]
1
1
0.485
1
0.907
0.038
logAlpha_re[10]
1
1
0.507
1
-0.636
0.034
logAlpha_re[11]
1
1
0.054
1
0.435
0.041
logAlpha_re[12]
1
1
0.512
1
0.532
0.030
logAlpha_re[13]
1
1
0.300
0
-0.038
0.031
logAlpha_re[14]
1
1
0.782
1
0.446
0.033
logAlpha_re[15]
1
1
0.292
0
0.361
0.036
logAlpha_re[16]
1
1
0.479
0
-0.052
0.032
logAlpha_re[17]
1
251
0.096
1
0.935
0.035
logAlpha_re[18]
1
1
0.357
1
0.518
0.042
logAlpha_re[19]
1
1
0.116
0
0.192
0.039
logAlpha_re[20]
1
1
0.262
0
-0.286
0.036
logAlpha_re[21]
1
1
0.144
1
0.794
0.034
logAlpha_re[22]
1
1
0.616
1
-0.627
0.037
logAlpha_re[23]
1
1
0.926
0
-0.144
0.034
logAlpha_re[24]
1
1
0.937
0
-0.052
0.037
logAlpha_re[25]
1
1
0.754
0
-0.036
0.032
tauobs[1]
1
1
0.971
1
0.996
0.012
tauobs[2]
1
501
0.061
1
3.306
0.043
tauobs[3]
1
1
0.286
1
3.325
0.039
tauobs[4]
1
1
0.994
1
4.309
0.043
tauobs[5]
1
1
0.230
1
2.543
0.028
tauobs[6]
1
1
0.139
1
4.054
0.044
tauobs[7]
1
1
0.124
1
1.742
0.019
tauobs[8]
1
1
0.333
1
3.124
0.042
tauobs[9]
1
1
0.262
1
4.165
0.049
tauobs[10]
1
1
0.783
1
2.367
0.023
tauobs[11]
1
1
0.424
1
1.811
0.025
tauobs[12]
1
1
0.567
1
5.315
0.054
tauobs[13]
1
1001
0.145
1
5.474
0.073
tauobs[14]
1
1
0.864
1
2.661
0.026
tauobs[15]
1
1
0.745
1
6.427
0.083
tauobs[16]
0
NA
0.006
NA
NA
NA
tauobs[17]
1
1
0.278
1
6.817
0.072
tauobs[18]
1
1
0.978
1
2.223
0.051
tauobs[19]
1
1
0.115
1
1.769
0.030
tauobs[20]
1
1
0.711
1
2.131
0.026
tauobs[21]
1
1
0.543
1
2.761
0.035
tauobs[22]
1
1
0.362
1
3.072
0.040
tauobs[23]
1
1
0.206
1
2.047
0.020
tauobs[24]
1
1
0.990
1
1.392
0.021
tauobs[25]
1
1
0.772
1
1.469
0.023
logSREP_sd
1
1
0.381
1
0.417
0.006
logAlpha_sd
1
1
0.305
1
0.204
0.007
logAlpha02
1
1
0.697
1
0.292
0.009
lp__
1
1
0.253
1
-774.481
0.640
Show Chain 2
stest
start
pvalue
htest
mean
halfwidth
b0[1]
1
1
0.342
1
8.946
0.010
b0[2]
1
1
0.509
1
1.048
0.015
bWA[1]
1
1
0.324
1
0.682
0.004
bWA[2]
1
1
0.960
1
0.281
0.009
logSREP_re[1]
1
1
0.463
1
0.756
0.038
logSREP_re[2]
1
1
0.490
0
-0.066
0.030
logSREP_re[3]
1
1
0.915
1
1.087
0.030
logSREP_re[4]
1
1
0.233
1
-0.992
0.026
logSREP_re[5]
1
1
0.424
1
-0.770
0.026
logSREP_re[6]
1
1
0.526
1
0.400
0.032
logSREP_re[7]
1
1
0.163
0
0.168
0.032
logSREP_re[8]
1
1
0.493
1
-1.072
0.034
logSREP_re[9]
1
1
0.122
1
0.744
0.031
logSREP_re[10]
1
1
0.527
1
0.410
0.025
logSREP_re[11]
1
1
0.733
1
-0.359
0.036
logSREP_re[12]
1
1
0.158
1
0.744
0.032
logSREP_re[13]
1
1
0.951
1
-0.480
0.025
logSREP_re[14]
1
1
0.250
1
0.915
0.029
logSREP_re[15]
1
1
0.096
0
0.286
0.030
logSREP_re[16]
1
1
0.213
1
1.603
0.029
logSREP_re[17]
1
1
0.808
0
0.024
0.039
logSREP_re[18]
1
1
0.700
1
-0.731
0.027
logSREP_re[19]
1
1
0.980
1
-0.558
0.028
logSREP_re[20]
1
1
0.317
1
-0.842
0.033
logSREP_re[21]
1
1
0.249
1
0.774
0.031
logSREP_re[22]
1
1
0.627
1
-1.275
0.028
logSREP_re[23]
1
1
0.902
1
0.357
0.027
logSREP_re[24]
1
1
0.884
1
-0.388
0.027
logSREP_re[25]
1
1
0.644
1
-0.558
0.024
logAlpha0
1
1
0.343
1
1.559
0.006
logAlpha_re[1]
1
1
0.320
1
-0.335
0.026
logAlpha_re[2]
1
1001
0.061
1
-0.698
0.041
logAlpha_re[3]
1
1
0.980
0
-0.199
0.026
logAlpha_re[4]
1
1
0.975
1
-0.387
0.027
logAlpha_re[5]
1
1
0.941
0
0.279
0.028
logAlpha_re[6]
1
1
0.783
1
-0.491
0.026
logAlpha_re[7]
1
1
0.765
0
-0.234
0.027
logAlpha_re[8]
1
1
0.410
0
-0.064
0.035
logAlpha_re[9]
1
501
0.236
1
0.892
0.035
logAlpha_re[10]
1
1
0.083
1
-0.619
0.027
logAlpha_re[11]
1
1
0.300
1
0.407
0.029
logAlpha_re[12]
1
501
0.161
1
0.536
0.032
logAlpha_re[13]
1
1
0.236
0
-0.046
0.025
logAlpha_re[14]
1
1
0.161
1
0.452
0.027
logAlpha_re[15]
1
1
0.181
1
0.340
0.028
logAlpha_re[16]
1
1
0.128
0
-0.094
0.029
logAlpha_re[17]
1
1
0.394
1
0.928
0.030
logAlpha_re[18]
1
1
0.208
1
0.450
0.031
logAlpha_re[19]
1
1
0.129
0
0.220
0.031
logAlpha_re[20]
1
1
0.879
1
-0.344
0.030
logAlpha_re[21]
1
501
0.054
1
0.804
0.037
logAlpha_re[22]
1
1
0.244
1
-0.699
0.034
logAlpha_re[23]
1
1
0.955
0
-0.166
0.025
logAlpha_re[24]
1
1
0.231
0
-0.056
0.026
logAlpha_re[25]
1
1
0.501
0
-0.011
0.027
tauobs[1]
1
1
0.496
1
0.994
0.010
tauobs[2]
1
1
0.783
1
3.343
0.032
tauobs[3]
1
1
0.528
1
3.306
0.030
tauobs[4]
1
1
0.064
1
4.311
0.042
tauobs[5]
1
1
0.598
1
2.533
0.025
tauobs[6]
1
1
0.921
1
4.054
0.035
tauobs[7]
1
1
0.241
1
1.730
0.015
tauobs[8]
1
1
0.454
1
3.082
0.034
tauobs[9]
1
1
0.097
1
4.191
0.041
tauobs[10]
1
251
0.071
1
2.381
0.019
tauobs[11]
1
1
0.247
1
1.795
0.021
tauobs[12]
1
1
0.381
1
5.367
0.045
tauobs[13]
1
1
0.771
1
5.352
0.046
tauobs[14]
1
1
0.290
1
2.637
0.022
tauobs[15]
1
751
0.056
1
6.385
0.074
tauobs[16]
1
1
0.685
1
4.584
0.053
tauobs[17]
1
1
0.135
1
6.774
0.060
tauobs[18]
1
1
0.817
1
2.191
0.044
tauobs[19]
1
1
0.827
1
1.768
0.026
tauobs[20]
1
1
0.401
1
2.135
0.023
tauobs[21]
1
1
0.491
1
2.779
0.028
tauobs[22]
1
1
0.289
1
3.090
0.034
tauobs[23]
1
1
0.713
1
2.042
0.014
tauobs[24]
1
1
0.071
1
1.371
0.016
tauobs[25]
1
1
0.472
1
1.479
0.020
logSREP_sd
1
1
0.209
1
0.410
0.006
logAlpha_sd
1
1
0.118
1
0.209
0.008
logAlpha02
1
1
0.642
1
0.291
0.009
lp__
1
1
0.080
1
-774.390
0.691
Show Chain 3
stest
start
pvalue
htest
mean
halfwidth
b0[1]
1
1
0.612
1
8.948
0.011
b0[2]
1
1
0.601
1
1.051
0.015
bWA[1]
1
1
0.453
1
0.683
0.004
bWA[2]
1
1
0.579
1
0.277
0.009
logSREP_re[1]
1
1
0.566
1
0.753
0.033
logSREP_re[2]
1
1
0.641
0
-0.071
0.030
logSREP_re[3]
1
1
0.357
1
1.074
0.031
logSREP_re[4]
1
1
0.868
1
-1.003
0.030
logSREP_re[5]
1
1
0.345
1
-0.768
0.030
logSREP_re[6]
1
1
0.180
1
0.404
0.030
logSREP_re[7]
1
1
0.158
0
0.166
0.032
logSREP_re[8]
1
1
0.140
1
-1.071
0.034
logSREP_re[9]
1
1
0.581
1
0.734
0.034
logSREP_re[10]
1
1
0.277
1
0.414
0.023
logSREP_re[11]
1
1
0.820
1
-0.367
0.036
logSREP_re[12]
1
1
0.662
1
0.737
0.026
logSREP_re[13]
1
1
0.724
1
-0.501
0.021
logSREP_re[14]
1
1
0.191
1
0.899
0.024
logSREP_re[15]
1
1
0.572
0
0.301
0.031
logSREP_re[16]
1
1
0.143
1
1.595
0.028
logSREP_re[17]
1
1
0.736
0
0.011
0.043
logSREP_re[18]
1
1
0.540
1
-0.743
0.030
logSREP_re[19]
1
1
0.415
1
-0.533
0.029
logSREP_re[20]
1
1
0.542
1
-0.838
0.032
logSREP_re[21]
1
1
0.788
1
0.778
0.026
logSREP_re[22]
1
1
0.278
1
-1.291
0.021
logSREP_re[23]
1
1
0.599
1
0.336
0.024
logSREP_re[24]
1
1
0.378
1
-0.391
0.025
logSREP_re[25]
1
1
0.282
1
-0.549
0.025
logAlpha0
1
1
0.362
1
1.557
0.006
logAlpha_re[1]
1
1
0.104
1
-0.331
0.028
logAlpha_re[2]
1
1
0.229
1
-0.735
0.032
logAlpha_re[3]
1
1
0.468
0
-0.189
0.026
logAlpha_re[4]
1
1
0.273
1
-0.364
0.027
logAlpha_re[5]
1
1
0.082
1
0.280
0.027
logAlpha_re[6]
1
1
0.700
1
-0.491
0.027
logAlpha_re[7]
1
1
0.295
0
-0.213
0.025
logAlpha_re[8]
1
1
0.679
0
-0.047
0.033
logAlpha_re[9]
1
1
0.153
1
0.905
0.033
logAlpha_re[10]
1
1
0.580
1
-0.621
0.026
logAlpha_re[11]
1
1
0.502
1
0.385
0.029
logAlpha_re[12]
1
1
0.793
1
0.566
0.028
logAlpha_re[13]
1
1
0.843
0
-0.031
0.025
logAlpha_re[14]
1
1
0.659
1
0.473
0.025
logAlpha_re[15]
1
1
0.203
1
0.342
0.028
logAlpha_re[16]
1
1
0.187
0
-0.061
0.026
logAlpha_re[17]
1
1
0.467
1
0.939
0.033
logAlpha_re[18]
1
1
0.858
1
0.483
0.032
logAlpha_re[19]
1
1
0.852
0
0.170
0.026
logAlpha_re[20]
1
1
0.390
1
-0.335
0.028
logAlpha_re[21]
1
1
0.726
1
0.792
0.032
logAlpha_re[22]
1
1
0.096
1
-0.666
0.032
logAlpha_re[23]
1
1
0.516
0
-0.165
0.026
logAlpha_re[24]
1
1
0.954
0
-0.039
0.025
logAlpha_re[25]
1
1
0.598
0
-0.027
0.026
tauobs[1]
1
1
0.957
1
0.994
0.010
tauobs[2]
1
1
0.845
1
3.324
0.032
tauobs[3]
1
1
0.216
1
3.302
0.030
tauobs[4]
1
1
0.403
1
4.353
0.039
tauobs[5]
1
1
0.389
1
2.538
0.023
tauobs[6]
1
1
0.335
1
4.033
0.036
tauobs[7]
1
251
0.249
1
1.721
0.015
tauobs[8]
1
1
0.830
1
3.127
0.034
tauobs[9]
1
1
0.875
1
4.155
0.038
tauobs[10]
1
251
0.065
1
2.383
0.021
tauobs[11]
1
1
0.724
1
1.772
0.018
tauobs[12]
1
1
0.102
1
5.338
0.045
tauobs[13]
1
1
0.797
1
5.424
0.042
tauobs[14]
1
1
0.973
1
2.660
0.020
tauobs[15]
1
1
0.281
1
6.486
0.063
tauobs[16]
1
1
0.323
1
4.568
0.047
tauobs[17]
1
1
0.172
1
6.783
0.052
tauobs[18]
1
1
0.953
1
2.192
0.042
tauobs[19]
0
NA
0.007
NA
NA
NA
tauobs[20]
1
1
0.603
1
2.136
0.019
tauobs[21]
1
1
0.588
1
2.750
0.026
tauobs[22]
1
501
0.276
1
3.109
0.033
tauobs[23]
1
1
0.103
1
2.054
0.015
tauobs[24]
1
1
0.826
1
1.385
0.015
tauobs[25]
1
1
0.421
1
1.454
0.019
logSREP_sd
1
1
0.827
1
0.413
0.007
logAlpha_sd
1
1
0.510
1
0.205
0.007
logAlpha02
1
1
0.334
1
0.291
0.008
lp__
1
1
0.910
1
-774.671
0.675
Show Chain 4
stest
start
pvalue
htest
mean
halfwidth
b0[1]
1
1
0.557
1
8.956
0.012
b0[2]
1
1
0.821
1
1.048
0.015
bWA[1]
1
1
0.634
1
0.679
0.004
bWA[2]
1
1
0.569
1
0.284
0.011
logSREP_re[1]
1
1
0.494
1
0.730
0.044
logSREP_re[2]
1
1
0.190
0
-0.077
0.032
logSREP_re[3]
1
1
0.532
1
1.097
0.034
logSREP_re[4]
1
751
0.207
1
-1.041
0.039
logSREP_re[5]
1
1
0.173
1
-0.790
0.031
logSREP_re[6]
1
1
0.880
1
0.381
0.037
logSREP_re[7]
1
1
0.487
0
0.134
0.037
logSREP_re[8]
1
1
0.766
1
-1.031
0.048
logSREP_re[9]
1
1
0.513
1
0.697
0.039
logSREP_re[10]
1
1
0.961
1
0.408
0.028
logSREP_re[11]
1
1
0.841
0
-0.353
0.043
logSREP_re[12]
1
1
0.677
1
0.732
0.033
logSREP_re[13]
1
1
0.378
1
-0.526
0.030
logSREP_re[14]
1
1
0.838
1
0.901
0.030
logSREP_re[15]
1
1
0.845
0
0.267
0.034
logSREP_re[16]
1
1
0.814
1
1.595
0.026
logSREP_re[17]
1
1
0.657
0
0.039
0.046
logSREP_re[18]
1
1
0.478
1
-0.715
0.032
logSREP_re[19]
1
1
0.779
1
-0.539
0.035
logSREP_re[20]
1
501
0.082
1
-0.789
0.044
logSREP_re[21]
1
1
0.841
1
0.787
0.034
logSREP_re[22]
1
1
0.487
1
-1.306
0.034
logSREP_re[23]
1
1
0.924
0
0.319
0.038
logSREP_re[24]
1
1
0.982
1
-0.392
0.034
logSREP_re[25]
1
1
0.750
1
-0.598
0.030
logAlpha0
1
1
0.172
1
1.556
0.007
logAlpha_re[1]
1
1
0.694
0
-0.313
0.034
logAlpha_re[2]
1
1
0.342
1
-0.729
0.037
logAlpha_re[3]
1
1
0.318
0
-0.196
0.033
logAlpha_re[4]
1
1
0.868
1
-0.371
0.036
logAlpha_re[5]
1
1
0.579
0
0.295
0.036
logAlpha_re[6]
1
1
0.630
1
-0.515
0.034
logAlpha_re[7]
1
1
0.644
0
-0.243
0.035
logAlpha_re[8]
1
1
0.712
0
-0.092
0.041
logAlpha_re[9]
1
1
0.462
1
0.924
0.037
logAlpha_re[10]
1
1
0.217
1
-0.613
0.038
logAlpha_re[11]
1
1
0.728
0
0.384
0.045
logAlpha_re[12]
1
1
0.080
1
0.566
0.032
logAlpha_re[13]
1
1
0.463
0
-0.011
0.033
logAlpha_re[14]
1
1
0.345
1
0.491
0.035
logAlpha_re[15]
1
1
0.105
0
0.355
0.038
logAlpha_re[16]
1
1
0.145
0
-0.069
0.030
logAlpha_re[17]
1
1
0.812
1
0.930
0.038
logAlpha_re[18]
1
1
0.411
1
0.472
0.040
logAlpha_re[19]
1
1
0.074
0
0.197
0.036
logAlpha_re[20]
1
1
0.109
0
-0.334
0.040
logAlpha_re[21]
1
1
0.719
1
0.788
0.038
logAlpha_re[22]
1
1
0.777
1
-0.702
0.038
logAlpha_re[23]
1
1
0.336
0
-0.151
0.036
logAlpha_re[24]
1
1
0.685
0
-0.029
0.036
logAlpha_re[25]
1
1
0.740
0
-0.020
0.036
tauobs[1]
1
1
0.844
1
1.002
0.014
tauobs[2]
1
1
0.579
1
3.321
0.037
tauobs[3]
1
1
0.702
1
3.305
0.039
tauobs[4]
1
1
0.531
1
4.364
0.049
tauobs[5]
1
1
0.082
1
2.569
0.031
tauobs[6]
1
1
0.495
1
4.033
0.049
tauobs[7]
1
1
0.784
1
1.725
0.021
tauobs[8]
1
1
0.909
1
3.040
0.043
tauobs[9]
1
1
0.725
1
4.163
0.043
tauobs[10]
1
1
0.758
1
2.375
0.022
tauobs[11]
1
1
0.388
1
1.768
0.022
tauobs[12]
1
1
0.737
1
5.341
0.058
tauobs[13]
1
1
0.817
1
5.397
0.059
tauobs[14]
1
1
0.866
1
2.648
0.029
tauobs[15]
1
1
0.647
1
6.407
0.083
tauobs[16]
1
1
0.773
1
4.524
0.065
tauobs[17]
1
1
0.569
1
6.694
0.069
tauobs[18]
1
1
0.647
1
2.150
0.053
tauobs[19]
1
1
0.552
1
1.749
0.033
tauobs[20]
1
1001
0.054
1
2.107
0.032
tauobs[21]
1
1
0.701
1
2.734
0.037
tauobs[22]
1
1
0.338
1
3.073
0.042
tauobs[23]
1
1
0.441
1
2.050
0.021
tauobs[24]
1
1
0.931
1
1.398
0.021
tauobs[25]
1
1
0.206
1
1.458
0.024
logSREP_sd
1
1
0.674
1
0.406
0.007
logAlpha_sd
1
1
0.192
1
0.209
0.007
logAlpha02
1
1
0.597
1
0.291
0.011
lp__
1
1
0.711
1
-775.272
0.649
Effective Sample Size
Show table
Parameter
EffectiveSize
b0[1]
2597.1
b0[2]
3107.5
bWA[1]
3646.1
bWA[2]
4274.1
logSREP_re[1]
5841.1
logSREP_re[2]
4046.5
logSREP_re[3]
4606.6
logSREP_re[4]
4578.9
logSREP_re[5]
5465.3
logSREP_re[6]
3613.4
logSREP_re[7]
3906.9
logSREP_re[8]
8284.0
logSREP_re[9]
4013.4
logSREP_re[10]
6704.3
logSREP_re[11]
6140.6
logSREP_re[12]
5289.2
logSREP_re[13]
5343.3
logSREP_re[14]
4455.1
logSREP_re[15]
3346.5
logSREP_re[16]
4431.9
logSREP_re[17]
3938.6
logSREP_re[18]
6651.0
logSREP_re[19]
7425.7
logSREP_re[20]
9238.4
logSREP_re[21]
7281.3
logSREP_re[22]
6692.6
logSREP_re[23]
10771.4
logSREP_re[24]
8050.0
logSREP_re[25]
8184.4
logAlpha0
4701.1
logAlpha_re[1]
15022.2
logAlpha_re[2]
10156.7
logAlpha_re[3]
13114.8
logAlpha_re[4]
12007.7
logAlpha_re[5]
11322.7
logAlpha_re[6]
11220.6
logAlpha_re[7]
12325.0
logAlpha_re[8]
8358.0
logAlpha_re[9]
8535.2
logAlpha_re[10]
9648.9
logAlpha_re[11]
12098.3
logAlpha_re[12]
10076.9
logAlpha_re[13]
11395.0
logAlpha_re[14]
12722.8
logAlpha_re[15]
13159.5
logAlpha_re[16]
14233.1
logAlpha_re[17]
8131.8
logAlpha_re[18]
11305.8
logAlpha_re[19]
13282.9
logAlpha_re[20]
10188.9
logAlpha_re[21]
9335.9
logAlpha_re[22]
10219.0
logAlpha_re[23]
10970.9
logAlpha_re[24]
15444.5
logAlpha_re[25]
15986.5
tauobs[1]
17575.5
tauobs[2]
13912.5
tauobs[3]
16397.0
tauobs[4]
15259.9
tauobs[5]
13875.1
tauobs[6]
15905.8
tauobs[7]
15872.5
tauobs[8]
11998.5
tauobs[9]
13559.6
tauobs[10]
16464.7
tauobs[11]
14120.5
tauobs[12]
15229.9
tauobs[13]
15306.8
tauobs[14]
15489.2
tauobs[15]
15856.1
tauobs[16]
16601.8
tauobs[17]
13658.5
tauobs[18]
9992.6
tauobs[19]
14112.2
tauobs[20]
14742.4
tauobs[21]
14751.4
tauobs[22]
14587.1
tauobs[23]
16304.2
tauobs[24]
14263.8
tauobs[25]
14523.4
logSREP_sd
3112.5
logAlpha_sd
2562.9
logAlpha02
4734.1
lp__
2428.7
Gelman Statistic
Show table
Parameter
Point est.
Upper C.I.
b0[1]
1.001
1.004
b0[2]
1.000
1.000
bWA[1]
1.001
1.002
bWA[2]
1.000
1.001
logSREP_re[1]
1.000
1.000
logSREP_re[2]
1.001
1.002
logSREP_re[3]
1.000
1.001
logSREP_re[4]
1.001
1.003
logSREP_re[5]
1.001
1.003
logSREP_re[6]
1.000
1.001
logSREP_re[7]
1.001
1.002
logSREP_re[8]
1.000
1.001
logSREP_re[9]
1.001
1.002
logSREP_re[10]
1.000
1.001
logSREP_re[11]
1.000
1.000
logSREP_re[12]
1.000
1.001
logSREP_re[13]
1.002
1.006
logSREP_re[14]
1.000
1.001
logSREP_re[15]
1.001
1.002
logSREP_re[16]
1.000
1.000
logSREP_re[17]
1.000
1.001
logSREP_re[18]
1.001
1.001
logSREP_re[19]
1.000
1.000
logSREP_re[20]
1.001
1.001
logSREP_re[21]
1.000
1.001
logSREP_re[22]
1.001
1.002
logSREP_re[23]
1.001
1.001
logSREP_re[24]
1.000
1.001
logSREP_re[25]
1.001
1.004
logAlpha0
1.000
1.001
logAlpha_re[1]
1.000
1.000
logAlpha_re[2]
1.000
1.000
logAlpha_re[3]
1.000
1.000
logAlpha_re[4]
1.000
1.000
logAlpha_re[5]
1.000
1.000
logAlpha_re[6]
1.000
1.001
logAlpha_re[7]
1.001
1.002
logAlpha_re[8]
1.000
1.001
logAlpha_re[9]
1.000
1.000
logAlpha_re[10]
1.000
1.001
logAlpha_re[11]
1.000
1.001
logAlpha_re[12]
1.000
1.001
logAlpha_re[13]
1.000
1.001
logAlpha_re[14]
1.001
1.001
logAlpha_re[15]
1.000
1.000
logAlpha_re[16]
1.000
1.001
logAlpha_re[17]
1.000
1.000
logAlpha_re[18]
1.001
1.002
logAlpha_re[19]
1.000
1.001
logAlpha_re[20]
1.001
1.002
logAlpha_re[21]
1.000
1.000
logAlpha_re[22]
1.001
1.003
logAlpha_re[23]
1.000
1.000
logAlpha_re[24]
1.000
1.000
logAlpha_re[25]
1.000
1.000
tauobs[1]
1.000
1.001
tauobs[2]
1.000
1.000
tauobs[3]
1.000
1.000
tauobs[4]
1.001
1.001
tauobs[5]
1.000
1.001
tauobs[6]
1.000
1.000
tauobs[7]
1.001
1.001
tauobs[8]
1.001
1.003
tauobs[9]
1.000
1.000
tauobs[10]
1.000
1.000
tauobs[11]
1.001
1.002
tauobs[12]
1.000
1.000
tauobs[13]
1.000
1.001
tauobs[14]
1.000
1.000
tauobs[15]
1.000
1.000
tauobs[16]
1.000
1.001
tauobs[17]
1.000
1.001
tauobs[18]
1.000
1.001
tauobs[19]
1.000
1.000
tauobs[20]
1.000
1.000
tauobs[21]
1.000
1.001
tauobs[22]
1.000
1.000
tauobs[23]
1.000
1.000
tauobs[24]
1.001
1.001
tauobs[25]
1.000
1.001
logSREP_sd
1.003
1.007
logAlpha_sd
1.001
1.002
logAlpha02
1.000
1.000
lp__
1.001
1.004
5. Prior Predictive Plots
Show figure
6. SR Curves
Show figure: Spawner-Recruit Curves
7. Posterior Residual Plots
Posterior P-Values of logRS Residuals
Show figure: Posterior P-Value for logRS
Where the posterior p-value represents the proportion of simulated means above the observed mean. In this case a value of 0.5414, which indicates a slightly higher bias of the simulated responses over the observations.
Posterior Distribution of logSREP residuals against WA and latitude
Show figure: Residuals of logSREP_re: Ocean TypeShow figure: Residuals of logSREP_re: Stream Type
Residuals of Posterior Predictive against Years and Observed Spawners
Show figure: Residuals of logRS by YearShow figure: Residuals of logRS by Spawners
Q-Q Norm Plot of Residuals
Show figure: QQNorm Plot of logRS
8. Residual Autocorrelation Plots
Show figure: Residuals ACF of Spawner-Recruit Relationship